Digital imaging systems have improved the process of creating, editing and rendering images. In particular, digital imaging systems have decreased the amount of processing time necessary to render an image. Nonetheless, the ability of digital imaging systems to process and, particularly, to render images remains limited by the memory capacity of digital imaging systems.
In digital imaging systems, an image is often divided into a rectangular grid defined by fixed spatial coordinates. Each grid element defines a sample point having one color. Each sample point is referred to as a picture element, commonly known as a pixel or a “dot” (not to be confused with the halftone dot used in the printing industry). Such an image is usually referred to as a raster image and is typically represented and stored in a format that uses several bits per pixel to identify the color of each pixel. The total amount of data necessary to represent an image depends on several factors, some of which include the image size, the resolution of the image, and the number of bits per pixel.
Large high-resolution images, particularly those containing “continuous tone,” “contone” or “CT” content (multiple bits per color component per pixel), require an extensive amount of data to represent the images. Because image rendering devices have limited memory and processing capacity, large high-resolution images often place a demand on image rendering devices that exceeds the image rendering capabilities of the devices. As an example, a typical Raster Image Processor (“RIP”) would not be able to handle the volume of printing format data in a 1200 dot per inch (“dpi”) image file represented in contone raster format. Such a file might contain the imaging data required for a map. For example, a file for printing a 32 inch by 44 inch sized image formed of 1200 dpi, 8 bits-per-pixel elements would require about 2 gigabytes of memory, well beyond that available to most rendering/RIP workstations. In many practical applications, such images consist of some photographic content and a large portion of “line work” (“LW”) data, i.e., text or geometric objects that delineate areas of constant color that are easily compressible, i.e., amendable to representation with a small number of bits per pixel.
One method of reducing the data volume of high resolution images is to divide the image into tiles, which can be handled more easily than full images. Rectangular portions of the tiles can then be “filled,” allowing the same information that was in a related portion of a raster image to be represented by a much smaller volume of data. An example of such a filling method is described in a U.S. Pat. No. 7,075,681 issued Jul. 11, 2006, titled SYSTEM AND METHOD FOR REDUCING THE DATA VOLUME OF IMAGES, the subject matter of which is hereby incorporated by reference. As described in this application, the “filled” rectangular portions are referred to as “rectfills.” More specifically, a rectfill is a rectangular area of an image filled with a single color. Filling is accomplished by defining the coordinates of the area and designating a single color with which to fill the area. The coordinates can define rectangular area as small as a single pixel or a rectangular area covering a large number of pixels. The single color can be defined in terms of CMYK or RBG values or a spot value and a possible transparency value. A further explanation at color values and rectfills is described in U.S. Publication No. 2004-0051884 published Mar. 18, 2004, titled METHOD OF ENCODING RASTER DATA BASED ON VARIATIONS OF COLOR, the subject matter which is hereby incorporated by reference. The drawback with employing rectfills to reduce data volume is that if the coordinates used to define the rectfill do not exactly coincide with pixel locations during rendering (perhaps due to the use of real numbers, the application of the output device rules or due to resizing an image, etc.), the wrong pixels may be filled by the rectfill color, resulting in gaps between rendered pixels or over-writing of other pixels as rendering “artifacts.”
Accordingly, there is a need for methods and apparatus for accurately rendering images that have been rectfill converted to have a reduced data volume. In particular, there is a need for methods and apparatus for accurately rendering rectfill reduced data volume images.